tbuckley/cabot-search huggingface dataset:
Neoadjuvant endocrine therapy for early-stage breast cancer: An institutional experience during the COVID19 pandemic.
Authors: Anne Christine Buteau, Mary D. Chamberlin, Steven Tau, Todd W. Miller
Journal: Journal of Clinical Oncology (2023)

e12625 Background: Neoadjuvant Endocrine therapy (NET) is under-utilized for locally advanced hormone receptor + breast cancer (BC) despite literature to date concluding that chemotherapy and NET have comparable response rates but with lower toxicity for the latter. The COVID19 pandemic presented a unique occasion to extend the use of NET for localized BC. Many surgical procedures across the country, including at Dartmouth-Hitchcock Medical Center (DHMC) were postponed in order to redirect resources. The American Society of Breast Surgeons released guidelines to manage patients in which surgery was delayed. For Estrogen Receptor + (ER+), HER2- early-stage tumors, the guidelines recommended NET as first-line treatment. Herein we review the rate of adoption of NET and patient/tumor characteristics associated with NET adoption at DHMC during the lockdown. Methods: A Retrospective analysis of patients diagnosed with early-stage ER+, HER2- BC between December 2019 and June 2020. Inclusion criteria included patients with non-metastatic hormone receptor + BC. Data extracted from chart review included age, menopausal status, tumor stage/grade, body mass index (BMI). A delay in surgery was defined as days between surgical consult and surgery over 14 days. Descriptive statistics will be applied to data collected on patient/tumor characteristics, and the number of patients accepting or declining NET. Results: 109 cases were identified within the study period with 42 cases designated as experiencing delays in surgery. The median age of the group (n = 42) was 62 and the majority was post-menopausal. 36 patients received NET with most started on an aromatase inhibitor. Median BMI was 28.5. Median duration of treatment was 39.5 days. Tumor response to NET was not clearly described in the pathology reports. Three cases were noted to have some decrease in cellularity for pathologic partial response and four had no definite response. Imaging modalities were compared to pathology size and MRI was found to be a fair predictor of size obtained from pathology specimens with a correlation metric of 0.78 indicating that it may serve as the best proxy for pre-treatment size. Pre-surgical MRIs were obtained in 51.3% of cases. The majority of patients on NET had no change in the grade of their pathology. During the course of the pandemic long-term adherence of adjuvant endocrine therapy decreased from 78% for those diagnosed in early 2020 to 56% for those diagnosed in late 2020. Conclusions: The COVID19 crisis was an unprecedented challenge to healthcare systems. Delays in surgery resulted in increased use of NET in early-stage BC. We plan to integrate other data including impact of NET on long-term adherence to endocrine therapy. Change in clinical vs pathologic stage will be reported for those with or without NET. This experience may help guide care during healthcare crises or in resources limited settings.
Effect of an artificial intelligence–powered programmed death-ligand 1 combined positive score analyzer in urothelial cancer on inter-observer and inter-site variability.
Authors: Soo Ick Cho, Jeong Hwan Park, Kyu Sang Lee, Euno Choi, Wonkyung Jung, Sanghoon Song, Sukjun Kim, Jisoo Shin, Jeongun Ryu, Aaron Valero Puche, Biagio Brattoli, Seonwook Park, Kyunghyun Paeng, Chan-Young Ock
Journal: Journal of Clinical Oncology (2023)

e13546 Background: Programmed death-ligand 1 (PD-L1) is a predictive marker for immune checkpoint inhibitors treatment response in urothelial carcinoma (UC). The combined positive score (CPS) is a representative method to evaluate the expression level of PD-L1 in UC. However, inter-observer and inter-institute variations can disrupt accurate CPS evaluation. The purpose of this study is to assess the role of an artificial intelligence (AI)-powered PD-L1 CPS analyzer on UC in reducing inter-observer and inter-institute variability. Methods: Lunit SCOPE PD-L1 CPS was developed with 4.94 x 10 5 tumor cells and 4.17 x 10 5 immune cells from 360 PD-L1 stained whole-slide images (WSIs) of UC from multiple institutions. The algorithm consisted of tissue area segmentation and cell detection AI models. The AI models calculated the CPS by detecting tumor cells over the tumor area and immune cells over the tumor and adjacent area. Three uropathologists from different university hospitals evaluated the CPS classification (≥10 or &lt;10) of 543 PD-L1 stained WSIs of UC from each hospital. The result with concordant CPS classification of each slide across ≥ 2 pathologists was considered the consensus. Each pathologist revisited to evaluate WSIs by referencing the AI model inference, after a washout period, if there was a discrepancy between the pathologist and the AI model. Results: Of 543 WSIs, 446 (82.1%) were classified as the same CPS subgroup by all three uropathologists. Also, pathologists had a high degree of concordance with the consensus in WSIs from their own hospitals. They re-evaluated 64, 73, and 75 WSIs with AI assistance, respectively, and changed the CPS classification for 47, 48, and 48 WSIs. After re-evaluation with AI assistance, three uropathologists agreed on the same CPS classification in 510 WSIs (93.9%). The overall percentage agreement (OPA) of each pathologist with the consensus increased from 95.0%, 94.8%, and 92.3% to 98.7%, 98.3%, and 96.9% after AI assistance, and the OPA for WSIs of other institutions increased more compared to the OPA for WSIs of their own hospital. Conclusions: This study shows that an AI-powered PD-L1 CPS analyzer in UC can reduce inter-observer and inter-site variability. This result suggests that the AI model will help evaluate CPS in UC more accurately and reduce variation in situations where pathologists analyze WSIs from unfamiliar institutions.[Table: see text]
Comparative overall survival of patients in trials of CKD4/6 inhibitors in combination with endocrine therapy in advanced breast cancer.
Authors: Coralea Kappel, Mitchell Elliott, Vikaash Kumar, Michelle B. Nadler, Alexandra Desnoyers, Eitan Amir
Journal: Journal of Clinical Oncology (2023)

1056 Background: CDK4/6i and endocrine therapy (ET) are an international gold standard therapy in estrogen receptor (ER) positive and HER2-negative advanced breast cancer (aBC). Individual trials of abemaciclib, palbociclib and ribociclib show similar impact on progression-free survival yet differing statistical significance for OS. A robust comparative evaluation of the efficacy, safety, and tolerability of the three drugs is warranted. Methods: We searched PubMed, ASCO, ESMO and SABCS proceedings to identify phase 3 randomized clinical trials reporting OS of CDK4/6i in combination with ET in ER-positive aBC in first or second line. A network meta-analysis using WinBUGS was performed to evaluate comparative efficacy, based on the ET partner, in the absence of direct comparisons. Trial level data on common and serious adverse events (AE) were extracted for each drug. The odds ratio (OR) for each AE was calculated comparing each CDK4/6i to the respective endocrine therapy backbone (aromatase inhibitor (AI) or fulvestrant) alone. Results: Seven studies comprising 4087 patients met the inclusion criteria. Median follow up was 70.2 months. Four studies paired a CDK4/6i with an AI or tamoxifen, 3 studies were paired with fulvestrant. There were no statistically significant differences in OS between any of the CDK4/6i with any ET backbone. Compared to palbociclib, ribociclib and abemaciclib showed significantly lower grade 1-2 fatigue/asthenia (ribociclib OR 0.69 [95% CI 0.58, 0.83]; abemaciclib OR 0.6 [95% CI 0.49, 0.74]) and abemaciclib showed significantly lower grade 1-2 alopecia (abemaciclib 0.76 [95% CI 0.6, 0.97]). Compared to palbociclib, ribociclib and abemaciclib showed significantly higher GI toxicity (ribociclib any grade vomiting OR 1.71 [95% CI 1.36, 2.15]; abemaciclib any grade vomiting OR 2.1 [95% CI 1.61, 2.62], any grade diarrhea 13.2 [95% CI 10.31, 16.78]). Treatment discontinuation was highest with abemaciclib. Conclusions: In this network meta-analysis, there was no statistically significant difference in OS between CDK4/6i despite differences in individual trials. Real-world data analyses may help to identify if a there is a meaningful inter-drug difference in efficacy. Significant differences between CDK4/6i are observed for safety and tolerability outcomes. [Table: see text]
Differences of prediction performances by multigene assays in patients with breast cancer with distinct genetic backgrounds.
Authors: Fei Xing, Ting-Hao Chen, Huimin Wang, Xuejiao Ma, Ran Ding, Zhongyu Lu
Journal: Journal of Clinical Oncology (2023)

526 Background: The 21-gene (Oncotype Dx), PAM50 (Prosigna) and Chinese-based 28-gene (RecurIndex) are validated genomic assays that provide prognostic information for distant recurrence risk in patients with hormone receptor (HR)-positive, HER2-negative early breast cancer (EBC). In this study, we aim to compare the differences in prediction performance by different multigene assays and gene expression profiles between the Eastern and the Western. Methods: Under the Affymetrix U133 platforms, the differences in gene expressions and risk scores from the Eastern population comprised of GSE20685 (n = 327) and part of GSE45255 (n = 95), and the Western population GSE25066 (n = 508) and the European in GSE45255(n = 44) were analyzed respectively. Patients were stratified by 21-gene Recurrence Scores (RS) of low (0-10), intermediate (11-25) and high (26-100) and categorized by ROR-S score of PAM50 of low (0-40), intermediate (41-60) and high (61-100). For 28-gene, a recurrence index for distant recurrence (RI-DR) cutoff of 33 was used to classify patients into high- and low-risk groups. Wilcoxon rank test was used to test for continuous variables and the chi-square test was used for categorical variables. Results: The Western population has significantly higher RS (85.9 vs. 65.5, p &lt; 0.001) and ROR-S (57.4 vs.53.7, p = 0.034) scores than the Eastern population. The distribution of 28-gene risk groups was significantly different between races with a higher proportion of high-risk in the Western (10.5% vs. 6.6%, p = 0.035). The low-risk patients in the Eastern stratified by 28-gene assay had significantly higher distant metastasis-free survival (DMFS) rates than high-risk (80% vs. 52%, p &lt; 0.001), which compared with Oncotype Dx RS (96% vs. 76%, p &lt; 0.001) and PAM50 ROR-S (93% vs. 72%, p &lt; 0.001). At the same time, the analysis of gene expressions revealed significantly high expressions of YWHAB, SF3B5, CKAP5, and DDX39A in the Eastern than that in the Western. However, most genes from 21-gene and ROR-S were more highly expressed in the Western than that in the Eastern. Conclusions: 21-gene and PAM50 provide the good performance and show similar patterns in the prognosis of EBC; However, the DMFS rate of the high-risk in the Eastern population determined by 28-gene was much lower than 21-gene and PAM50, which means there are bigger chemo-benefit in the high-risk from 28-gene. In conclusion, 28-gene has better performance in the prognosis of the Eastern population and is a more suitable tool for Eastern breast cancer patients.
Association of circulating tumor DNA with survival in neoadjuvant-treated triple negative breast cancer independent of pathological complete response.
Authors: Yan Li, Xiaobin Chen, Yuxiang Lin, Fangmeng Fu, Chuan Wang
Journal: Journal of Clinical Oncology (2023)

e12588 Background: Many patients with triple negative breast cancer (TNBC) still succumb to their disease after systematic treatment. It is crucial to identify those at high risk. Circulating tumor DNA (ctDNA) has recently become a promising marker to monitor tumor burden. Our study aimed to evaluate the clinical value of the presence and dynamic change of ctDNA to predict the tumor response and prognosis in TNBC patients treated with neoadjuvant chemotherapy (NAC). Methods: Cell-free DNA (cfDNA) was isolated from pre-NAC plasma samples and then analyzed by duplex sequencing. A Chi-square test or Fisher exact test were used for categorical variables. Kaplan‐Meier analysis with a Log‐rank test and the Cox proportional hazard regression model were used for the univariate and multivariable survival analysis. Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Results: A total of 82 TNBC patients receiving NAC were enrolled. Baseline ctDNA detection indicated significantly worse survival. TP53 and genes involved in PI3K-Akt signaling pathway mutations were significantly associated with survival independent of the pathological complete response (pCR). A TP53 detection combined clinical model is highly predictive, with a time-dependent ROC of 0.936 for 3-year overall survival (OS), compared to the model including clinical variables only (AUC of the clinical predictor: 0.751, P= 0.028). Conclusions: Baseline ctDNA detection, TP53 mutations, and genes involved in PI3K-Akt signaling pathway mutation are all independent prognostic factors in TNBC patients receiving NAC. A TP53 detection combining model is highly predictive.
Novel machine learning algorithm to predict response to immunotherapy in patients with small cell and non-small cell lung cancer.
Authors: Lakshya Sharma, Sunyoung Choi, Vaishnavi Balaji, Adwait Joshi, Iosif Mporas, Jiaqi Shi, Yi Zhao, Vipula Tailor, Riyaz N.H. Shah, Justin S. Waters, Sola Michael Adeleke
Journal: Journal of Clinical Oncology (2023)

e13556 Background: Immune checkpoint inhibitors (ICI) are used to manage patients with both small cell (SCLC) and non-small cell (NSCLC) lung cancer. However, ICI response rates are often low, and identifying patients that will benefit from ICIs can be challenging. The value of biomarkers used to predict ICI response, such as PD-L1, Combined Positive Score (CPS) or tumor mutational burden (TMB) have been debated. Furthermore, the resources needed to assess these biomarkers may not be available in many centres. Developing more accurate and accessible tools that predict ICI responses could enable a precision medicine approach that improves patient outcomes. This study aimed to use a novel machine-learning (ML) algorithm to predict response to different ICI therapies in patients with lung cancer based on clinically available data. Methods: 334 eligible records were cleaned and reprocessed from textual to categorical data using one hot encoding. Complete datasets were available for 161 patients. Differences in the data distribution were handled using the Synthetic Minority Oversampling Technique. Six ML algorithms were trained, including Linear regression, Support Vector Classifier, XGBoost Classifier, Random Forest, Decision Tree, and Gaussian Naive Bayes Classifier. These algorithms used 80% of the training data, were tested on 20% of validation data and used the Grid Search Cross-Validation technique for hyperparameter optimization. Results: For the 161 patients included in the final analysis, the mean age was 68 years and 48% were female. 9% of patients had SCLC and 80% had NSCLC. Patients receiving Pembrolizumab, Nivolumab and Atezolizumab comprised 62%, 11% and 25%, respectively. The artificial intelligence (AI) algorithm predicted and stratified ICI response better than PD-L1 levels. Of the ML algorithms, XGBoost Classifier predicted response with the most accuracy, 64% (0.61 F1 score). This model found that good performance status (0-1), female gender and adenocarcinoma sub-type predicted response to ICI. On the other hand, M1, N2 staging, male gender, squamous cell carcinoma sub-type and receiving Atezolizumab were predictive of disease progression. Conclusions: This study developed multiple novel ML models to predict responses to ICIs in lung cancer. XGBoost Classifier used clinically available data to show that the type of ICI a patient receives, their histopathology sub-type and their TMN staging impact ICI response. Future work will aim to improve accuracy and predict ICI toxicity by including data from multiple centres, different cancer types and additional clinical variables. [Table: see text]
Prevalence and patterns of breast white adipose tissue inflammation among American Black women and indigenous Nigerian women with early-stage breast cancer.
Authors: Bethina Liu, Sherry Shen, Oluwatosin Zainab Omoyiola, Oluwole O. Odujoko, Avinash Sharma, Gbenga Samson Ogunleye, Israel Adeyemi Owoade, Olalekan Olasehinde, Funmilola Olanike Wuraola, Peter Ntiamoah, Jessica Flynn, Yuan Chen, Dilip D. Giri, Andrea Carpio, Rivka Kahn, Cassandra Chang, Monica Morrow, Olusegun Isaac Alatise, T. Peter Kingham, Neil M. Iyengar
Journal: Journal of Clinical Oncology (2023)

e12560 Background: Racial disparities in breast cancer outcomes pose a major public health challenge. Complex interactions between social determinants of health and tumor biology, mediated by obesity and adiposity, may play an important role in these disparities. White adipose tissue inflammation (WATi) of the breast, which is histologically defined by the presence of crown-like structures (CLS-B), occurs in the setting of excess adiposity and promotes tumor growth via elevated levels of aromatase, proinflammatory growth factors, and metabolic dysregulation. Here we examined relationships between race, body mass index (BMI), and WATi in two cohorts of patients with early-stage breast cancer: 1) American Black women and 2) indigenous Nigerian women. Methods: Non-tumor breast WAT specimens were collected from patients undergoing mastectomy for treatment or prevention of breast cancer at Memorial Sloan Kettering Cancer Center (MSK) in the US and Obafemi Awolowo University Teaching Hospital (OAUTH) in Nigeria. WATi was defined by the presence of CLS-B as detected by CD68 immunostaining. All tissue analyses were performed at MSK. Associations among BMI and WATi were tested within each cohort using Kendall’s tau rank correlation and compared between cohorts using Pearson's Chi-squared test. Results: A total of 224 American women were enrolled at MSK between 2011-2018, of whom 39 self-reported Black race. At OAUTH, 97 Nigerian women were enrolled between 2018-2022. Invasive carcinoma was present in 32 American Blacks and 97 Nigerians. Of those with invasive carcinoma, the prevalence of WATi among American Black women was 72% vs. 37% in the Nigerian cohort ( p&lt;0.001). In the US cohort, there was no significant difference between the prevalence of WATi among Black vs. non-Black patients (72% vs. 62%; p=0.3). 44% of American Blacks were obese and 34% overweight compared with 27% and 39% of Nigerians, respectively (p=0.07). The prevalence of WATi varied by BMI category within each cohort, as shown. Overweight/obesity was associated with WATi in the Nigerian (p=0.009) and American cohorts (p&lt;0.001); a trend was observed in the American Black subgroup (p=0.13). Conclusions: Breast WATi and obesity were more prevalent among American Black women with breast cancer compared with Nigerian women. These findings highlight the need for further investigation of environment, lifestyle patterns, and body composition differences between indigenous and diaspora populations. [Table: see text]
Cardiac function of long-responders to dual anti-HER2 antibodies amongst patients with HER2 positive advanced breast cancer.
Authors: Kelvin K H Bao, Jeffrey CH Chan, Leone Sutanto, Jocelyn G Chan, Ka Man Cheung, Harry HY Yiu
Journal: Journal of Clinical Oncology (2023)

1043 Background: Dual anti-HER2 antibodies pertuzumab (P) and trastuzumab (T) in combination with taxane (D), followed by PT maintenance, is the standard first line treatment for HER2 positive advanced breast cancer (HER2+ ABC). Treatment associated cardiotoxicity necessitates regular cardiac function surveillance, which is a burden particularly for treatment long-responders. Data for cardiac safety of prolonged P+T exposure is scarce. We investigate the real-world impact on cardiac function in long-responders to treatment with dual anti-HER2 antibodies. Methods: We identified consecutive patients with HER2+ ABC who received the CLEOPATRA regimen (PT-D) between Jan 2014 and Dec 2020 from an institutional cancer registry. All patients had pre-treatment multiple-gated acquisition (MUGA) scan or echocardiogram, and subsequently at 3-monthly intervals until end of treatment to monitor left ventricular ejection fraction (LVEF). Patients on treatment for ≥36 months were considered long-responders. The Wilcoxon signed-rank test was used to assess any significant difference in LVEF at various landmark time-points in comparison to their pretreatment baseline. Results: 101 women with HER2+ ABC were eligible for analysis. Median age at treatment was 62 (IQR, 56.0-69.0). The median duration of treatment was 17.3 months (IQR, 9.0-31.3). 22.8% of patients were long-responders, who received a median of 67 cycles of treatment (IQR, 58-88). Compared to baseline, median LVEF was significantly decreased at 6m (median, 66% vs 69%, p=0.02), however there were no significant differences for any of the subsequent time-points up to 84 m. All of the larger LVEF drop (≥10% from baseline) occurred by the first 24 months, representing 4.7% of the overall measurements. Risk factors present for patients experienced treatment suspension (n=3) included previous exposures to anthracycline and left sided radiotherapy. Conclusions: In patients with HER2+ ABC who were long-responders to first-line PT-D, prolonged exposure to dual anti-HER2 antibodies was not associated with significant cardiotoxicity. It is safe to de-escalate the cardiac surveillance for this population. [Table: see text]
Clustering whole-exome sequences of breast cancer and associations with staging and molecular subtype.
Authors: Kingson Man, Alice Lim, Zahra Eftekhari, Chi Wah Wong
Journal: Journal of Clinical Oncology (2023)

e12563 Background: Large-scale genetic sequencing of breast cancer has enabled modern approaches to precision medicine, with the discovery of a handful of variants now known to be associated with breast cancer. However, it is critical to identify additional gene variants in breast cancer that are associated with clinically relevant features of cancers, such as staging and molecular subtype. Methods: We took an unsupervised machine learning approach that clustered the somatic whole exome sequences (WES) of 1533 breast cancers. We performed k-modes clustering on the binarized mutational state of the top 250 most frequently mutated genes. Following two rounds of clustering, 11 distinct “barcodes” for each genetic cluster’s mutation profile became apparent. We systematically tested each genetically defined cluster for associations with molecular subtypes of breast cancer. We performed non-parametric significance testing by randomly permuting cluster assignments to generate an empirical null distribution of the effect of clustering on proportions of the clinical factor of interest. Results: As an example of our set of results, two clusters showed roughly three-fold enrichment of triple-negative breast cancer (TNBC) patients, compared to the whole-group proportion. We calculated SHAP values to provide model explainability and identify the genes that placed a cancer into a particular cluster; TP53 and TTN were the strongest drivers in relation to TNBC. Genetic clusters were also found to associate with T-, N-, and M-stages. Conclusions: Our approach, which uses unsupervised machine learning on WES to create genetic groups of cancers, considers the joint mutational state – present or absent – of multiple genes for their clinical relevance. This reveals many additional variants that may have been previously overlooked or of uncertain significance.